DeepFakes for Privacy: Investigating Perceptions and Effectiveness of State-of-the-Art Privacy-Enhancing Face Obfuscation Methods

Khamis, M. , Farzand, H., Mumm, M. and Marky, K. (2022) DeepFakes for Privacy: Investigating Perceptions and Effectiveness of State-of-the-Art Privacy-Enhancing Face Obfuscation Methods. In: 2022 International Conference on Advanced Visual Interfaces (AVI 2022), Rome, Italy, 06-10 Jun 2022, p. 21. ISBN 9781450397193 (doi: 10.1145/3531073.3531125)

Abstract

There are many contexts in which a person’s face needs to be obfuscated for privacy, such as in social media posts. We present a user-centered analysis of the effectiveness of DeepFakes for obfuscation using synthetically generated faces, and compare it with state-of-the-art obfuscation methods: blurring, masking, pixelating, and replacement with avatars. For this, we conducted an online survey (N=110) and found that DeepFake obfuscation is a viable alternative to state-of-the-art obfuscation methods; it is as effective as masking and avatar obfuscation in concealing the identities of individuals in photos. At the same time, DeepFakes blend well with surroundings and are as aesthetically pleasing as blurring and pixelating. We discuss how DeepFake obfuscation can enhance privacy protection without negatively impacting the photo’s aesthetics.

• Item Type: Conference Proceedings
• Additional Information: This work was supported an EPSRC New Investigator Award (grant number EP/V008870/1), and by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has also been funded by the UK EPSRC under grant number EP/S035362/1. This publication was partially supported by the Excellence Bursary Award by the University of Glasgow.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marky, Dr Karola and Mumm, Marija and Farzand, Ms Habiba and Khamis, Dr Mohamed
• Authors: Khamis, M., Farzand, H., Mumm, M., and Marky, K.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450397193
• Copyright Holders: Copyright © 2022 ACM
• First Published: First published in AVI 2022, Article 21
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
310627		TAPS: Assessing, Mitigating and Raising Awareness of the Security and Privacy Risks of Thermal Imaging	Mohamed Khamis	Engineering and Physical Sciences Research Council (EPSRC)	EP/V008870/1	Computing Science
313490		Preventing THErmal ATtacks using AI-driven Approaches	Mohamed Khamis	Engineering and Physical Sciences Research Council (EPSRC)	5676417 -PETRAS	Computing Science